Multimode fiber laser cavities as nonlinear optical processors

TL;DR

This paper demonstrates that multimode fiber laser cavities can serve as efficient, low-power nonlinear optical processors for machine learning, transforming input data into class-separable patterns with high accuracy.

Contribution

It introduces a novel use of multimode fiber laser cavities as physical nonlinear processors for optical machine learning, reducing complexity and power consumption.

Findings

Achieved 85-99% accuracy on diverse benchmarks.

Enabled data transformation with fewer trainable parameters than neural networks.

Showed stability and scalability of the optical processing approach.

Abstract

Optical computing provides a promising path toward energy-efficient machine learning, yet implementing nonlinear transformations without complex electronics or high-power sources remains challenging. Here, we demonstrate that continuous-wave multimode fiber laser cavities can function as nonlinear optical processors. Input images encoded as phase patterns on a spatial light modulator undergo high-dimensional transformation through the interplay of multimode interference and gain saturation dynamics. The cavity maps input data into spatially stable, class-separable intensity distributions, enabling a simple linear classifier to achieve accuracies of 85--99\% across diverse benchmarks -- including medical imaging and remote sensing -- with orders of magnitude fewer trainable parameters than deep neural networks. Our results establish multimode fiber lasers as compact, low-power physical processors for scalable optical machine learning.